WO2002089305A1 - Detection de palier moteur defectueux par analyse par ondelettes de l'onde mobile du courant de demarrage - Google Patents

Detection de palier moteur defectueux par analyse par ondelettes de l'onde mobile du courant de demarrage Download PDF

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Publication number
WO2002089305A1
WO2002089305A1 PCT/US2002/013626 US0213626W WO02089305A1 WO 2002089305 A1 WO2002089305 A1 WO 2002089305A1 US 0213626 W US0213626 W US 0213626W WO 02089305 A1 WO02089305 A1 WO 02089305A1
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WIPO (PCT)
Prior art keywords
bearing
data
motor
analyzing
current transient
Prior art date
Application number
PCT/US2002/013626
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English (en)
Inventor
Michael J. Devaney
Levent Eren
Original Assignee
Square D Company
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Square D Company filed Critical Square D Company
Priority to DE60236668T priority Critical patent/DE60236668D1/de
Priority to EP02729075A priority patent/EP1421670B1/fr
Publication of WO2002089305A1 publication Critical patent/WO2002089305A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G01R31/343—Testing dynamo-electric machines in operation

Definitions

  • Induction machines are called the workhorses of industry due to their widespread use in manufacturing. The heavy reliance of industry on these machines in critical applications makes catastrophic motor failures very expensive. Vibration, thermal, and acoustic analyses are some of the commonly used methods, for predictive maintenance, to monitor the health of the machine to prevent motor failures from causing expensive shutdowns. Preventive maintenance of induction motors plays an important role in avoiding expensive shut-downs due to motor failures. Vibration and thermal monitoring require additional sensors or transducers to be fitted on the machines. While some large motors may already come with vibration and thermal transducers, it is not economically or physically feasible to provide the same for smaller machines.
  • MCSA Motor current signature analysis
  • MCSA provides a non-intrusive way to obtain information about bearing health using already available line current.
  • MCSA gets bearing information by relating the current spectral frequencies to characteristic vibration frequencies. Vibration signals from a defective bearing often consist of a superposition of normal bearing noise and the impulse response due to the defects.
  • the characteristic vibration frequencies are calculated using rotor speed and the bearing geometry.
  • MCSA investigates steady state data and utilizes the Fourier Analysis as the primary frequency domain method in determining bearing related spectral components. Presently, motor current signature analysis techniques cannot detect bearing faults until the bearing fault reaches advanced stages. This increases the risk of a catastrophic failure.
  • a method for detecting motor bearing defects comprises obtaining motor current transient data during motor start-up, and analyzing the motor current transient data to detect changes in RMS levels due to bearing defect-induced resonance.
  • an apparatus for detecting motor bearing defects comprises means for obtaining motor current transient data during motor start-up, and means for analyzing the motor current transient data to detect changes in RMS levels due to bearing defect-induced resonance.
  • FIG. 1 shows a rolling element bearing geometry
  • FIG. 2 illustrates an analysis of part of a dyadic filter bank
  • FIG. 3 shows a two channel polyphase filter structure
  • FIG. 4 shows a motor start transient current under 1/2 hp load
  • FIG. 5 shows a normalized current rms values tor half load in the example of FIG.
  • FIG. 6 shows the difference of normalized current rms values in the example of
  • FIG. 4; FIG. 7 shows a motor start transient current under no load
  • FIG. 8 shows a normalized current rms values for no load
  • FIG. 9 shows the difference of normalized current rms values in the example of
  • the characteristic vibration frequencies can be calculated using equations 1-5.
  • ⁇ is the contact angle in radians and f- m is the rotor speed in Hz.
  • Outer race defect frequency foo the ball passing frequency on the outer race, is given by
  • Inner race defect frequency / ID 7 2 / m (l- ⁇ £ 7 r ⁇ cos 0 [1]
  • the characteristic current frequencies / CF due to bearing characteristic vibration frequencies are calculated by [1] where / is the power line frequency.
  • the equations 1-5 can be used to calculate the current harmonics due to faulty bearings. These components are very small compared to harmonic multiples of the power frequency and are thus difficult to detect.
  • the analysis of the starting current transient signal is proposed.
  • This analysis is performed via the discrete wavelet transform.
  • the wavelet transform provides better performance in transient analysis than the Fourier transform.
  • the frequency sub-bands for bearing pre-fault and post-fault conditions are compared to identify the effects of bearing/machine resonant frequencies as the motor starts, As the motor starts, it will go through the whole range of speeds from stopped to its operating speed. This range includes critical speeds where the bearing induced vibrations are more pronounced because of mechanical resonances. The higher vibration magnitude at these speeds makes the detection of bearing faults easier.
  • the starting current transient of an induction machine is analyzed via discrete wavelet transform and the magnitudes of frequency subbands are compared to the baseline data collected with a healthy set of bearings to detect any changes in bearing condition.
  • the greatest magnitude deviation from the baseline data occurs at critical speeds when faulty bearing condition exists.
  • the motor starting current transient is captured using a conventional circuit or power monitor.
  • the current wave of a single phase is sampled at 512 points per cycle for 60 cycles using the Waveform CaptureTM feature of a Square D CM4000 series monitor.
  • Waveform Capture TM is triggered by a high speed overcurrent alarm.
  • the data is transferred to a PC where this data is analyzed by a matlab program to decompose the signal into its wavelet subbands. After the decomposition of signal into its subbands, the magnitudes of the selected subbands are compared to the baseline data to detect any changes in bearing condition.
  • One principal advantage of this approach is that is does not require access to the machine itself as the vibration based bearing monitors do.
  • FIG. 2 depicts the basic structure of the dyadic filter banks employed in discrete wavelet transform.
  • Digital Butterworth, Chebyshev and elliptic filters obtained from the classical analog filters via the bilinear transformation can be designed as two-channel filter banks composed of allpass filters.
  • the two channel polyphase filter structure used is shown in FIG. 3.
  • the allpass polyphase filter banks result in less computational complexity than that of the original Butterworth filter banks. Therefore, using the filter bank in FIG. 3 instead the one in FIG. 2 reduces the computational effort.
  • the IIR filters are chosen for their better frequency selectivity and lower computational complexity over their FIR counterparts.
  • a three phase, 1 hp, 200 V, 60 Hz, 1750 RPM, four-pole induction motor was used (US Motors Frame 143T).
  • the shaft end ball bearing is a 6205-2Z-J/C3 (9 balls) and the opposite end ball bearing is a 6203-2Z-J/C3 (9 balls).
  • a single 1.5 mm diameter hole was drilled in the outer race of the opposite end bearing.
  • FIG. 5 Indicates the results of the transient wavelet analysis for the pre-fault and post-fault conditions. In each case, three trials were average and the plotted values were normalized by 0-120 Hz frequency bands at each cycle. Because of the relative dominance of the 60 Hz fundamental, the lowest sub-band of 0-120 HZ is not depicted. A comparison of the two plots indicates a substantial difference at the 11 th cycle for the 240-480 Hz frequency band in FIG. 5. The motor reaches the critical speed in this cycle relative to the defect.
  • the difference of the normalized RMS values are plotted in FIG. 6. Basically, the normalized healthy bearing current RMS values are subtracted from the normalized faulty bearing current RMS values to obtain this plot.
  • the motor start transient current is plotted in FIG. 7. Comparing starting transients for half load and no load conditions, the motor reaches critical speed faster under no load condition. The point of critical speed can be assessed by examining the ratio of cycle RMS to steady state RMS under different loading conditions. This ratio is about 2X.
  • Figure 8 depicts the normalized RMS values for the first 16 cycles. A comparison of the two plots indicates a substantial difference at the 4 th cycle for the upper three frequency bands.
  • the difference of the normalized RMS values are plotted in FIG. 9. Basically, the normalized healthy bearing current RMS values are subtracted from the normalized faulty bearing current RMS values to obtain this plot.

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  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • General Physics & Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

L'invention concerne un procédé permettant de détecter des défauts de palier moteur, consistant à obtenir des données concernant l'onde mobile du courant du moteur pendant le démarrage du moteur; puis, à analyser ces données afin de détecter des changements de niveaux de valeurs efficaces dus à la résonance provoquée par le défaut de palier.
PCT/US2002/013626 2001-05-01 2002-04-30 Detection de palier moteur defectueux par analyse par ondelettes de l'onde mobile du courant de demarrage WO2002089305A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
DE60236668T DE60236668D1 (de) 2001-05-01 2002-04-30 Erkennung von motorlagerschaden über wavelet analyse des einschaltstromimpulses
EP02729075A EP1421670B1 (fr) 2001-05-01 2002-04-30 Detection de palier moteur defectueux par analyse par ondelettes de l'onde mobile du courant de demarrage

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US09/846,865 US6727725B2 (en) 2001-05-01 2001-05-01 Motor bearing damage detection via wavelet analysis of the starting current transient
US09/846,865 2001-05-01

Publications (1)

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WO2002089305A1 true WO2002089305A1 (fr) 2002-11-07

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PCT/US2002/013626 WO2002089305A1 (fr) 2001-05-01 2002-04-30 Detection de palier moteur defectueux par analyse par ondelettes de l'onde mobile du courant de demarrage

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US (1) US6727725B2 (fr)
EP (1) EP1421670B1 (fr)
DE (1) DE60236668D1 (fr)
WO (1) WO2002089305A1 (fr)

Cited By (12)

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US6727725B2 (en) 2001-05-01 2004-04-27 Square D Company Motor bearing damage detection via wavelet analysis of the starting current transient
EP1970691A1 (fr) * 2007-03-12 2008-09-17 Snecma Procédé de détection d'un endommagement d'un roulement de palier d'un moteur
WO2009063118A1 (fr) * 2007-11-14 2009-05-22 Universitat Politècnica De Catalunya Système pour le diagnostic de pannes dans des moteurs électriques
EP2156159A1 (fr) * 2007-06-04 2010-02-24 Eaton Corporation Système et procédé pour porter une détection de défaut en utilisant une annulation de bruit de courant de stator
ES2336875A1 (es) * 2007-11-14 2010-04-16 Universitat Politecnica De Catalunya Metodo y equipo de diagnostico de fallos en motores electricos de corriente alterna.
WO2011006528A1 (fr) * 2009-07-13 2011-01-20 Abb Research Ltd Détection de défauts dans une machine électrique tournante
FR2952177A1 (fr) * 2009-11-04 2011-05-06 Snecma Procede de detection d'un endommagement d'au moins un roulement de palier d'un moteur
WO2011104091A1 (fr) * 2010-02-24 2011-09-01 Siemens Aktiengesellschaft Système ou procédé de détermination de l'état d'un palier
EP2728367A1 (fr) * 2012-11-01 2014-05-07 ABB Research Ltd. Procédé de détection d'une panne dans une machine électrique
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US6727725B2 (en) 2001-05-01 2004-04-27 Square D Company Motor bearing damage detection via wavelet analysis of the starting current transient
US7770458B2 (en) 2007-03-12 2010-08-10 Snecma Method of detecting damage to an engine bearing
EP1970691A1 (fr) * 2007-03-12 2008-09-17 Snecma Procédé de détection d'un endommagement d'un roulement de palier d'un moteur
FR2913769A1 (fr) * 2007-03-12 2008-09-19 Snecma Sa Procede de detection d'un endommagement d'un roulement de palier d'un moteur
EP2455772A3 (fr) * 2007-06-04 2012-10-10 Eaton Corporation Système et procédé pour la détection de défaut de roulement utilisant l'annulation du bruit de courant de stator
EP2156159A1 (fr) * 2007-06-04 2010-02-24 Eaton Corporation Système et procédé pour porter une détection de défaut en utilisant une annulation de bruit de courant de stator
EP2156159A4 (fr) * 2007-06-04 2010-08-18 Eaton Corp Système et procédé pour porter une détection de défaut en utilisant une annulation de bruit de courant de stator
WO2009063118A1 (fr) * 2007-11-14 2009-05-22 Universitat Politècnica De Catalunya Système pour le diagnostic de pannes dans des moteurs électriques
ES2336875A1 (es) * 2007-11-14 2010-04-16 Universitat Politecnica De Catalunya Metodo y equipo de diagnostico de fallos en motores electricos de corriente alterna.
WO2011006528A1 (fr) * 2009-07-13 2011-01-20 Abb Research Ltd Détection de défauts dans une machine électrique tournante
US9032803B2 (en) 2009-11-04 2015-05-19 Snecma Method for detecting damage in at least one engine roller bearing
FR2952177A1 (fr) * 2009-11-04 2011-05-06 Snecma Procede de detection d'un endommagement d'au moins un roulement de palier d'un moteur
WO2011054867A1 (fr) * 2009-11-04 2011-05-12 Snecma Procédé de détection d'un endommagement d'au moins un roulement de palier d'un moteur
WO2011104091A1 (fr) * 2010-02-24 2011-09-01 Siemens Aktiengesellschaft Système ou procédé de détermination de l'état d'un palier
CN102770744A (zh) * 2010-02-24 2012-11-07 西门子公司 用于确定轴承状态的系统或方法
CN102770744B (zh) * 2010-02-24 2016-01-20 西门子公司 用于确定轴承状态的系统或方法
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DE60236668D1 (de) 2010-07-22
US6727725B2 (en) 2004-04-27
US20020186039A1 (en) 2002-12-12
EP1421670B1 (fr) 2010-06-09

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